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Line 1: {{Short description\|Functions used to evaluate optimization algorithms}} In applied mathematics, '''test functions''', known as '''artificial landscapes''', are useful to evaluate characteristics of optimization algorithms, such as: convergence rate, precision, robustness and general performance. * convergence rate * precision * robustness * general performance Here some test functions are presented with the aim of giving an idea about the different situations that optimization algorithms have to face when coping with these kinds of problems. In the first part, some objective functions for single-objective optimization cases are presented. In the second part, test functions with their respective [[Pareto front\|Pareto fronts]] for [[multi-objective optimization]] problems (MOP) are given.

Test functions for optimization: Difference between revisions